SuPoolVisor: a visual analytics system for mining pool surveillance

被引:23
作者
Xia, Jia-zhi [1 ]
Zhang, Yu-hong [1 ]
Ye, Hui [1 ]
Wang, Ying [1 ]
Jiang, Guang [1 ]
Zhao, Ying [1 ]
Xie, Cong [2 ]
Kui, Xiao-yan [1 ]
Liao, Sheng-hui [1 ]
Wang, Wei-ping [1 ]
机构
[1] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China
[2] Facebook, New York, NY 10003 USA
基金
中国国家自然科学基金;
关键词
Bitcoin mining pool; Visual analytics; Transaction data; Visual reasoning; FinTech; TP39; BITCOIN; VISUALIZATION; EXPLORATION; QUALITY;
D O I
10.1631/FITEE.1900532
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade. The mining pool, which is considered the source of Bitcoin, is the cornerstone of market stability. The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues. However, the anonymity of mining-pool miners and the difficulty of analyzing large numbers of transactions limit in-depth analysis. It is also a challenge to achieve intuitive and comprehensive monitoring of multi-source heterogeneous data. In this study, we present SuPoolVisor, an interactive visual analytics system that supports surveillance of the mining pool and de-anonymization by visual reasoning. SuPoolVisor is divided into pool level and address level. At the pool level, we use a sorted stream graph to illustrate the evolution of computing power of pools over time, and glyphs are designed in two other views to demonstrate the influence scope of the mining pool and the migration of pool members. At the address level, we use a force-directed graph and a massive sequence view to present the dynamic address network in the mining pool. Particularly, these two views, together with the Radviz view, support an iterative visual reasoning process for de-anonymization of pool members and provide interactions for cross-view analysis and identity marking. Effectiveness and usability of SuPoolVisor are demonstrated using three cases, in which we cooperate closely with experts in this field.
引用
收藏
页码:507 / 523
页数:17
相关论文
共 67 条
[21]  
Di Battista G., 2015, IEEE Symp. on Visualization for Cyber Security VizSec, P1
[22]  
Fleder M., 2015, BITCOIN T GRAPH ANAL, Vabs/1502.01657
[23]   Decentralization in Bitcoin and Ethereum Networks [J].
Gencer, Adem Efe ;
Basu, Soumya ;
Eyal, Ittay ;
van Renesse, Robbert ;
Sirer, Emin Gun .
FINANCIAL CRYPTOGRAPHY AND DATA SECURITY, FC 2018, 2018, 10957 :439-457
[24]   Emergency Medicine Back on the Front Line against the Emerging Risk 2019-nCoV [J].
Hausfater, P. ;
Na, N. ;
Zhao, Y. .
ANNALES FRANCAISES DE MEDECINE D URGENCE, 2020, 10 (01) :1-2
[25]   DNA visual and analytic data mining [J].
Hoffman, P ;
Grinstein, G ;
Marx, K ;
Grosse, I ;
Stanley, E .
VISUALIZATION '97 - PROCEEDINGS, 1997, :437-+
[26]   Computational Simulation of the Activation Cycle of Gα Subunit in the G Protein Cycle Using an Elastic Network Model [J].
Kim, Min Hyeok ;
Kim, Young Jin ;
Kim, Hee Ryung ;
Jeon, Tae-Joon ;
Choi, Jae Boong ;
Chung, Ka Young ;
Kim, Moon Ki .
PLOS ONE, 2016, 11 (08)
[27]  
Kinkeldey C., 2017, POSTERS EUROVIS 2017
[28]   An Analysis of Anonymity in Bitcoin Using P2P Network Traffic [J].
Koshy, Philip ;
Koshy, Diana ;
McDaniel, Patrick .
FINANCIAL CRYPTOGRAPHY AND DATA SECURITY, FC 2014, 2014, 8437 :469-485
[29]  
Kroll J.A., 2013, Proceedings of WEIS
[30]  
Lewenberg Y, 2015, PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15), P919